Maximizing Summer: Reflecting on Last Year’s College And Career Readiness Data

During the school year, administrators’ and counselors’ days are filled with seemingly unending to-do lists, bureaucratic responsibilities, and unanticipated crises that emerge. There is rarely time to pause and thoughtfully reflect on how things are really going beyond achievement test scores and graduation rates. Slow, deep thinking requires time and perspective. And the summer months–a time that combines knowledge of how the past efforts have gone and the hopes for the next school year–are the most appropriate time to seek understanding that will inform plans and produce more effective action. 

With their more flexible schedule and a reprieve in the typical intensity of the school year, these days during the summer provide a key opportunity to review more nuanced data, consider the implications for which programs and efforts are working and which could use more attention, celebrate the successes, and set goals to continue momentum and improve in critical areas. Leaders, and their staff, have time for individual reflection. These individual reflections should be discussed and shared among leadership and relevant teams, honing the insights and co-developing a prioritized set of actionable insights that will inform communications, resource allocations, professional learning, and related actions. This process of analysis, reflection, goal-setting, and planning can be crucial to cultivating an effective college and career culture. ‍

Reflecting on The Previous School Year

In assessing the past school year’s successes and identifying areas for growth and improvement, it is important to use data points that will inform which areas are working well and which areas are concerning as well as those that will help enlighten why some of these trends are occurring. Thinking creatively beyond the typical high-level reports can be especially beneficial in uncovering areas of need and potential for growth. 

While data never tells the whole story of a community, the story of a learning community cannot be told without using available data as an important component. Selecting which available data to use, how to weight its relevance, and what the analytics mean is the work of leadership. This begins in selecting which data elements are most relevant to improving the college and career readiness culture and outcomes.

Data and Metrics to Consider 

Achievement Data

This is data that shows absolute and relative student performance against national, state, and local expectations in content specific or general knowledge performance.

• Grade Point Average (GPA)
• Graduation Rates
• Percentage of Students Not On-Track to Graduate on Time
• College Preparatory Course Rates
• Postsecondary Plans & Next Steps for Graduating Seniors

Student Engagement & School Climate Data

This is data which gauges how connected and active community members, especially students, are to the activities of the school and how the school supports them. 

• School Attendance
• Behavioral or Disciplinary Infractions
• Extracurricular Participation
• Community Service and Volunteer Hours Completion 

College and Career Readiness Data

This data measures students’ development for, and accomplishment of, postsecondary success and the degree to which the school has robust resources to provide support for students in preparation for their post-graduation endeavors.

• Participation in Career-Readiness or Work-Based Learning Programs
• Rates of Industry Certifications, Micro-Credentials, Seals, and other Work-Based Learning Endorsements
• FAFSA Completion Rates
• Correlations between CCR Plan and Expected Matriculation
• Attendance at CCR Planning Events (e.g., college or career fairs, career tours, financial aid informational sessions)
• Usage Rates, and duration of usage, of a CCR platform 
• Number of Industry Partners  

Tools & Approaches for Data Analysis 

It is important to use a common set of data types to draw inferences and engage in analysis–especially when a broader team will be collaborating on decisions or implementing plans based on the data. However, performing analysis is not a skillset that is necessarily shared by all educators or administrators on a leadership team. Therefore, very often a team will select one or more colleagues who will perform the initial analysis and provide material to the broader team for consideration. The most basic level of such analysis is a static report which combines narrative, tables, and graphs. But this approach is usually insufficient to promote creative reflection. Rather, the data analyst can package data in tools that allow for any team member to sort, inquire, and play with the data in an interactive fashion.

Data Analysis Tools

‍There are a variety of tools which are available to produce interactive data exploration resources. Districts utilizing Microsoft and Google likely have such capability already. For example, Microsoft Office 365 has Excel which can be used to create sortable tables, pivot tables, dynamic graphs, and similar user interfaces. Institutional Office 365 subscriptions often provide licenses for Power BI which allows for multiple data feeds to be combined into a simple interface. Some districts have procured access to more sophisticated tools such as SAS, Tableau, and other specialized data analytic tools. 

LMS, SIS, or CCR Platform Capabilities‍

Many districts have installed modern learning management systems (LMSs) or student information systems (SISs) that already have data analytic capabilities. Most of the leading providers of LMS and SIS have included some degree of open data analytics. If a district or school has a college and career platform, such as SchooLinks, they could use the data gathered and tracked within that system to analyze key CCR metrics. In short, it is important for the leadership to ensure that they, or their team members performing the analysis, have tools available, and very often these tools are already present.

To derive actionable insights from reflecting on data and associated analytics requires a framing, or a particular cognitive lens, through which the reflection occurs. Selecting initial framings, and using multiple lenses, adds depth to the resulting understanding in both validity and limitations. Some examples of possible approaches are provided below:

Snapshot and longitudinal‍

Knowing how students are performing, or any other metric, at a given point in time is a snapshot view. This enables questions such as How does the attendance of our 9th graders compare to our 11th graders this past school year? or How did the 9th grade attendance of the Class of 2023 compare to the 9th grade attendance of the Class of 2026? Longitudinal analysis examines how a given metric changes over time for the same cohort of students.  An example is How did the GPA for CTE students of the Class of 2023 change from their 9th grade through their 12th grade year?

Using a demographic or other single attribute as the common thread for analysis‍

This approach is familiar to many educators as it uses a common descriptor–grade band, gender, ethnicity, free or reduced meal program participation, or other–as a way to frame comparisons on metrics. A useful approach is to utilize an outcome characteristic, such as membership in the lowest or highest performing quartile of a state assessment as the unifying descriptor to examine a subpopulation or subgroup. 

Broaden the demographic descriptors‍

Related to the single attribute analysis, non-academic descriptors can be used as an analytic lens. Examples include the participation in specific extracurricular programs, non-participation in extracurricular activities, participation in internship or apprenticeship experiences, enrollment in specific school feeder pattern combinations, or the grade level in which a particular critical course, such as Algebra 1, is completed.

Postsecondary data‍

Ideally, schools would have access to data on how graduating cohorts have engaged with work, school, the military, or other activities in their post-secondary lives. This data is most often unavailable, and even when available is not complete.  However, when available this is a rich source of insight. A CCR platform that allows for alumni network connections can be a great resource for collecting this kind of data.

Read Part 2 - Utilizing Insights to Inform Goals